Abstract
Discussions of motor behavior have traditionally focused on how a nervous system controls a body. However, it has become increasingly clear that a broader perspective, in which motor behavior is seen as arising from the interaction between neural and biomechanical dynamics, is needed. This chapter reviews a line of work aimed at exploring this perspective in a simple model of walking. Specifically, I describe the evolution of neural pattern generators for a hexapod body, present a neuromechanical analysis of the dynamics of the evolved agents, characterize how the neural and biomechanical constraints structure the fitness space for this task, and examine the impact of network architecture.
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References
Bäck, T. (1996). Evolutionary Algorithms in Theory and Practice. Oxford University Press.
Beer, R.D. (1990). Intelligence as Adaptive Behavior: An Experiment in Computational Neuroethology. Academic Press.
Beer, R.D. (1995a). A dynamical systems perspective on agent-environment interaction. Artificial Intelligence 72:173–215.
Beer, R.D. (1995b). On the dynamics of small continuous-time recurrent neural networks. Adaptive Behavior 3:469–509.
Beer, R.D. (1996). Toward the evolution of dynamical neural networks for minimally cognitive behavior. In P. Maes, M. Mataric, J.A. Meyer, J. Pollack and S. Wilson (Eds.), From Animals to Animats 4: Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior (pp. 421–429). MIT Press.
*Beer, R.D. (1997). The dynamics of adaptive behavior: A research program. Robotics and Autonomous Systems 20:257–289.
Beer, R.D. (2006). Parameter space structure of continuous-time recurrent neural networks. Neural Computation 18:3009–3051.
Beer, R.D., Chiel, H.J. and Gallagher, J.C. (1999). Evolution and analysis of model CPGs for walking II. General principles and individual variability. Journal of Computational Neuroscience 7:119–147.
Beer, R.D. and Gallagher, J.C. (1992). Evolving dynamical neural networks for adaptive behavior. Adaptive Behavior 1:91–122.
Brooks, R.A. (1991). New approaches to robotics. Science 253:1227–1232.
*Chiel, H.J. and Beer, R.D. (1997). The brain has a body: Adaptive behavior emerges from interactions of nervous system, body and environment. Trends in Neurosciences 20:553–557.
Chiel, H.J., Beer, R.D. and Gallagher, J.C. (1999). Evolution and analysis of model CPGs for walking I. Dynamical modules. Journal of Computational Neuroscience 7:99–118.
Clark, A. (1997). Being There: Putting Brain, Body and World Together Again. MIT Press.
Cliff, D., Harvey, I. and Husbands, P. (1993). Explorations in evolutionary robotics. Adaptive Behavior 2:73–110.
Gallagher, J.C., Beer, R.D., Espenschied, K.S. and Quinn, R.D. (1996). Applications of evolved locomotion controllers to a hexapod robot. Robotics and Autonomous Systems 19(1):95–103.
Gibson, J.J. (1979). The Ecological Approach to Visual Perception. Lawrence Erlbaum.
*Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley.
Goldman, M.S., Golowasch, J., Marder, M. and Abbott, L.F. (2001). Global structure, robustness and modulation of neuronal models. Journal of Neuroscience 21:5229–5238.
Golowasch, J., Goldman, M.S., Abbott, L.F. and Marder, E. (2002). Failure of averaging in the construction of a conductance-based neural model. Journal of Neurophysiology 87:1129–1131.
Haschke, R. and Steil, J.J. (2005). Input space bifurcation manifolds of recurrent neural networks. Neurocomputing 64C:25–38.
Kier, R.J., Ames, J.C., Beer, R.D., and Harrison, R.R. (2006). Design and implementation of multipattern generators in analog VLSI. IEEE Transactions on Neural Networks 17(4):1025–1038.
Kimura, M. and Nakano, R. (1998). Learning dynamical systems by recurrent neural networks from orbits. Neural Networks 11:1589–1599.
Mathayomchan, B. and Beer, R.D. (2002). Center-crossing recurrent neural networks for the evolution of rhythmic behavior. Neural Computation 14:2043–2051.
*Mitchell, M. (1996). An Introduction to Genetic Algorithms. MIT Press.
*Nolfi, S. and Floreano, D. (2000). Evolutionary Robotics. MIT Press.
Prinz, A.A., Bucher, D. and Marder, E. (2004). Similar network activity from disparate circuit parameters. Nature Neuroscience 7:1345–1352.
Psujek, S., Ames, J. and Beer, R.D. (2006). Connection and coordination: The interplay between architecture and dynamics in evolved model pattern generators. Neural Computation 18:729–747.
Seys, C.W. and Beer, R.D. (2004). Evolving walking: The anatomy of an evolutionary search. In S. Schaal, A. Ijspeert, A. Billard, S. Vijayakumar, J. Hallam and J.-A. Meyer (Eds.), From Animals to Animats 8: Proceedings of the Eighth International Conference on the Simulation of Adaptive Behavior (pp. 357–363). MIT Press.
Synder, V. (2005). Analysis of the Biomechanics and Neural Control of Two Kinetic Models of the Buccal Mass of Aplysia. M.S. Thesis (Advisor: Hillel Chiel), Dept. of Biology, Case Western Reserve University.
Thelen, E. and Smith, L.B. (1994). A Dynamic Systems Approach to the Development of Perception and Action. MIT Press.
Winters, J.M. and Crago, P.E. (Eds.). (2000). Biomechanics and Neural Control of Posture and Movement. Springer.
The references marked with an asterisk (*) are specifically recommended for further introduction or background to the topic.
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Beer, R.D. (2009). Beyond Control: The Dynamics of Brain-Body-Environment Interaction in Motor Systems. In: Sternad, D. (eds) Progress in Motor Control. Advances in Experimental Medicine and Biology, vol 629. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-77064-2_2
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DOI: https://doi.org/10.1007/978-0-387-77064-2_2
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